# Can Large Language Models Match the Conclusions of Systematic Reviews?

arXiv: https://arxiv.org/abs/2505.22787 · HTML: https://mlsanity.com/paper/2505.22787/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 58 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 23 of 23)

- [Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs](https://mlsanity.com/paper/2306.13063/) (2023)
- [Long-context LLMs Struggle with Long In-context Learning](https://mlsanity.com/paper/2404.02060/) (2024)
- [HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs](https://mlsanity.com/paper/2412.18925/) (2024)
- [Can large language models reason about medical questions?](https://mlsanity.com/paper/2207.08143/) (2022)
- [ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidence](https://mlsanity.com/paper/2404.10198/) (2024)
- [Taming Overconfidence in LLMs: Reward Calibration in RLHF](https://mlsanity.com/paper/2410.09724/) (2024)
- [Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases](https://mlsanity.com/paper/2012.07675/) (2020)
- [What Evidence Do Language Models Find Convincing?](https://mlsanity.com/paper/2402.11782/) (2024)
- [ConflictBank: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLM](https://mlsanity.com/paper/2408.12076/) (2024)
- [MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records](https://mlsanity.com/paper/2308.14089/) (2023)
- [Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization](https://mlsanity.com/paper/2008.11293/) (2020)
- [Summarizing, Simplifying, and Synthesizing Medical Evidence Using GPT-3 (with Varying Success)](https://mlsanity.com/paper/2305.06299/) (2023)
- [HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking](https://mlsanity.com/paper/2309.08503/) (2023)
- [BIOMEDICA: An Open Biomedical Image-Caption Archive, Dataset, and Vision-Language Models Derived from Scientific Literature](https://mlsanity.com/paper/2501.07171/) (2025)
- [Large Language Models are overconfident and amplify human bias](https://mlsanity.com/paper/2505.02151/) (2025)
- [Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature](https://mlsanity.com/paper/2310.16146/) (2023)
- [Fine-Tuning is Fine, if Calibrated](https://mlsanity.com/paper/2409.16223/) (2024)
- [Long Context is Not Long at All: A Prospector of Long-Dependency Data for Large Language Models](https://mlsanity.com/paper/2405.17915/) (2024)
- [Accelerating Clinical Evidence Synthesis with Large Language Models](https://mlsanity.com/paper/2406.17755/) (2024)
- [Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report](https://mlsanity.com/paper/2402.01733/) (2024)
- [FineTuneBench: How well do commercial fine-tuning APIs infuse knowledge into LLMs?](https://mlsanity.com/paper/2411.05059/) (2024)
- [MedREQAL: Examining Medical Knowledge Recall of Large Language Models via Question Answering](https://mlsanity.com/paper/2406.05845/) (2024)
- [Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models](https://mlsanity.com/paper/2404.03577/) (2024)

## Built on by (most cited first, 8 of 8)

- [Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap](https://mlsanity.com/paper/2608.05179/) (2026)
- [PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR](https://mlsanity.com/paper/2601.18207/) (2026)
- [Faithfulness vs. Safety: Evaluating LLM Behavior Under Counterfactual Medical Evidence](https://mlsanity.com/paper/2601.11886/) (2026)
- [This Treatment Works, Right? Evaluating LLM Sensitivity to Patient Question Framing in Medical QA](https://mlsanity.com/paper/2604.05051/) (2026)
- [Evaluating Large Language Models for Evidence-Based Clinical Question Answering](https://mlsanity.com/paper/2509.10843/) (2025)
- [Can AI Agents Synthesize Scientific Conclusions?](https://mlsanity.com/paper/2606.11337/) (2026)
- [Treatment, evidence, imitation, and chat](https://mlsanity.com/paper/2506.23040/) (2025)
- [Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison](https://mlsanity.com/paper/2606.05436/) (2026)

## Abstract (arXiv)

Systematic reviews (SR), in which experts summarize and analyze evidence across individual studies to provide insights on a specialized topic, are a cornerstone for evidence-based clinical decision-making, research, and policy. Given the exponential growth of scientific articles, there is growing interest in using large language models (LLMs) to automate SR generation. However, the ability of LLMs to critically assess evidence and reason across multiple documents to provide recommendations at the same proficiency as domain experts remains poorly characterized. We therefore ask: Can LLMs match the conclusions of systematic reviews written by clinical experts when given access to the same studies? To explore this question, we present MedEvidence, a benchmark pairing findings from 100 SRs with the studies they are based on. We benchmark 24 LLMs on MedEvidence, including reasoning, non-reasoning, medical specialist, and models across varying sizes (from 7B-700B). Through our systematic evaluation, we find that reasoning does not necessarily improve performance, larger models do not consistently yield greater gains, and knowledge-based fine-tuning degrades accuracy on MedEvidence. Instead, most models exhibit similar behavior: performance tends to degrade as token length increases, their responses show overconfidence, and, contrary to human experts, all models show a lack of scientific skepticism toward low-quality findings. These results suggest that more work is still required before LLMs can reliably match the observations from expert-conducted SRs, even though these systems are already deployed and being used by clinicians. We release our codebase and benchmark to the broader research community to further investigate LLM-based SR systems.